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STATPSY FINALS > REGRESSION > Flashcards

REGRESSION Flashcards

1
Q

statistical technique for finding the best-fitting straight line for
a set of data

A

regression

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2
Q

the best-fitting straight line for
a set of data or resulting straight line is
called

A

regression line

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3
Q

Y=bX+a

A

Linear Equation

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4
Q

Y=bX+a

A

Regression Equation

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5
Q

Y=bX+a what is the slope

A

b

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6
Q

determines how much the Y variable changes when X is
increased by one point.

A

slope (b)

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7
Q

Y=bX+a The value of a in the general equation is called

A

Y-intercept

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8
Q

it determines the value of Y when X = 0

A

a or the Y-intercept

(Y=bX+a)

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9
Q

On a graph, the _ value identifies the point where the line intercepts the Y-axis

A

a (Y=bX+a)

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10
Q

means that Y increases when X is increased, what slope

A

positive slope

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11
Q

indicates that Y decreases when X is increased, what slope

A

negative slope

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12
Q

regression equation for Y is the _ equation

A

linear equation

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13
Q

distance between the actual data point (Y) and the predicted point on the line (Ŷ) is defined as

formula:

A

Y – Ŷ

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14
Q

The Regression Equation for Prediction

Ŷ =

A

Ŷ = bX + a

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15
Q

The goal of _ is to find the equation for the line that minimizes these (Y – Ŷ) distances.

A

regression

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16
Q

gives a measure of the standard distance between the predicted Y values on the regression line and the actual Y values in the data.

A

standard error of estimate

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17
Q

process of testing the significance of a regression equation and is very similar to the analysis of variance (ANOVA)

analysis of

A

analysis of regression

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18
Q

The variability for the original Y scores (both SS and df) is partitioned into _
components

A

TWO

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19
Q

(1) the variability that is predicted by the regression
equation and
(2) the residual variability

A

two components of variability for the original Y scores

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20
Q

(1) the variability that is predicted by the regression equation

A

components of variability for the original Y scores

21
Q

(2) the residual variability

A

components of variability for the original Y scores

22
Q

The slope of the regression equation (b or beta) is zero. what hypotheses in analysis of regression?

A

Ho

23
Q

The slope of the regression equation (b or beta) is not zero. what hypotheses in analysis of regression?

A

H1

24
Q

Variable X significantly predicts variable Y. what hypotheses in analysis of regression?

A

H1

25
Q

process of using several predictor variables to help obtain
more accurate predictions.

A

Multiple Regression

26
Q

different _ variables are related to each other, which means that they are often measuring and predicting the same thing.

A

predictor variables

27
Q

Is adding more variables to the equation always better?

A

no

28
Q

the variables may _ with each other, adding another predictor variable to a regression equation does not always add to the accuracy of prediction.

A

variables may overlap with each other

29
Q

Regression Equations with Two Predictors

A

Multiple Regression

30
Q

Ŷ = b₁X₁ + b₂x₂ + a

A

Regression Equations with Two Predictors/Multiple Regression

31
Q

describes the proportion of the total variability of the Y scores that is accounted for by the regression equation.

A

R²

32
Q

can be defined as the standard distance between the predicted Y values (from the regression equation) and the actual Y values (in the data)

A

standard error of
estimate

33
Q

use to determine whether the equation predicts a significant portion of the variance for the Y scores.

A

F-ratio

34
Q

determines the value of Y when X = 0

A

a = Constant or the Y-intercept

35
Q

_ analysis evaluates the contribution of each predictor variable after the influence of the other predictor has been considered.

A

regression analysis

(Partial Correlations (β))

36
Q

you can determine whether each predictor variable contributes to the relationship by itself or simply duplicates the contribution already made by another variable.

A

Partial Correlations (β) (regression analysis)

37
Q

R², F value (F), degrees of freedom (numerator, denominator; in parentheses separated by a comma next to F), and significance level (p), β. Report the β and the corresponding t-test for that predictors for each predictor in the regression. (R²=.358, F(2,55)=5.56, p<.01). (β = .56, p<.001), as did agreeableness ((β= -.36, p<.01).

A

Reporting Results Regression

38
Q

_ table presents the analysis of regression evaluating the significance of the regression equation, including the F-ratio and the level of significance (the p value or alpha level for the test).

A

ANOVA TABLE

39
Q

summarizes the unstandardized and the standardized coefficients for the regression equation.

_ table

A

Coefficients table

40
Q

The standardized coefficients are the _ values

A

beta (b) values.

41
Q

For one predictor, beta is simply
the _ correlation between X and Y.

A

Pearson Correlation

42
Q

the table uses a _ statistic to evaluate the significance of each predictor variable. For one predictor variable, this is identical to the significance of the regression equation and you should find that t is equal to the square root of the F-ratio from the analysis of regression.

A

t statistic

43
Q

For two predictor variables, the t values measure the _ of the contribution of each variable beyond what is already predicted by the other variable.

A

significance

44
Q

On a graph, it identifies the point where the line intercepts the Y-axis

A

Y-intercept/Constant

45
Q

is the actual data point

A

Y

46
Q

the predicted point on the line
(the straight line)

A

Ŷ (Y hat)

47
Q

each data point is an _ of X and Y

A

intersection

48
Q

actual data point may be _ from the computed regression equation

A

different

49
Q

perfect correlation

A

correlation is r = +1.00

STATPSY FINALS (9 decks)

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